Specular filter (sf) and dip oriented partial imaging (dopi) seismic migration

ABSTRACT

According to one embodiment, subsurface ray directions in beam migration or subsurface wave propagation directions in reverse time migrations are used to obtain additional Specular Filter (SF) and Dip Oriented Partial Imaging (DOPI) images. SF migration applies a specular imaging condition during migration with a pre-specified subsurface dip field. It boosts the S/N ratio in both images and gathers, by effectively removing migration noise. DOPI images are produced by decomposing a standard migration image according to subsurface dip inclination or/and dip azimuth groups, providing various views of the subsurface image. Both SF and DOPI migration images can supply valuable additional information compared to a standard migration image, and they can be efficiently generated during migration.

CROSS REFERENCE TO RELATED APPLICATION

This application claims the benefit of U.S. Provisional Application No.61/807,570 filed Apr. 2, 2013, claims the benefit of U.S. ProvisionalApplication No. 61/836,740 filed Jun. 19, 2013, and is a divisionalapplication of U.S. application Ser. No. 14/230,722, entitled “SPECULARFILTER (SF) AND DIP ORIENTED PARTIAL IMAGING (DOPI) SEISMIC MIGRATION,”and filed Mar. 31, 2014, now U.S. Pat. No. 9,651,694 which issued on May16, 2017 herein incorporated by reference in its entirety for allpurposes.

TECHNICAL FIELD

This disclosure relates to the general subject of seismic signalprocessing and, in particular, to methods for using seismic data toimage the subsurface for purposes of seismic exploration and/orsurveillance.

BACKGROUND

A seismic survey represents an attempt to image or map the subsurface ofthe earth by sending sound energy down into the ground and recording the“echoes” that return from the rock layers below. The source of thedown-going sound energy might come, for example, from explosions orseismic vibrators on land, or air guns in marine environments. During aseismic survey, the energy source is placed at various locations nearthe surface of the earth above a geologic structure of interest. Eachtime the source is activated, it generates a seismic signal that travelsdownward through the earth. “Echoes” of that signal are then recorded ata great many locations on the surface. Multiple source/recordingcombinations are then combined to create a near continuous profile ofthe subsurface that can extend for many miles. In a two-dimensional(2-D) seismic survey, the recording locations are generally laid outalong a single line, whereas in a three dimensional (3-D) survey therecording locations are distributed across the surface in a gridpattern. In simplest terms, a 2-D seismic line can be thought of asgiving a cross sectional picture (vertical slice) of the earth layers asthey exist directly beneath the recording locations. A 3-D surveyproduces a data “cube” or volume that is, at least conceptually, a 3-Dpicture of the subsurface that lies beneath the survey area. In reality,though, both 2-D and 3-D surveys interrogate some volume of earth lyingbeneath the area covered by the survey. Finally, a 4-D (or time-lapse)survey is one that is recorded over the same area at two or moredifferent times. Obviously, if successive images of the subsurface arecompared any changes that are observed (assuming differences in thesource signature, receivers, recorders, ambient noise conditions, etc.,are accounted for) will be attributable to changes in the subsurface.

A seismic survey is composed of a very large number of individualseismic recordings or traces. The digital samples in seismic data tracesare usually acquired at 0.002 second (2 millisecond or “ms”) intervals,although 4 millisecond and 1 millisecond sampling intervals are alsocommon. Typical trace lengths when conventional impulsive sources areused are 5-16 seconds, which corresponds to 2500-8000 samples at a2-millisecond interval. If a non-impulsive source is used, the extendedactivation time of the source needs to be accommodated for, so the tracelengths will generally be longer. Conventionally each trace records oneseismic source activation, so there is one trace for each live sourcelocation-receiver activation. In some instances, multiple physicalsources might be activated simultaneously but the composite sourcesignal will be referred to as a “source” herein, whether generated byone or many physical sources.

In a typical 2-D survey, there will usually be several tens of thousandsof traces, whereas in a 3-D survey the number of individual traces mayrun into the multiple millions of traces.

Of course, the ultimate goal in acquiring seismic data proximate to anexploration target is to obtain an image representative of thesubsurface. To that end, a variety of seismic data processing algorithmshave been developed and are routinely utilized. One of the mostimportant of these is seismic migration which is designed to correct,among other things, the arrival time and position of reflectors in theseismic section to make them more closely match the corresponding layerconfiguration in the subsurface. Seismic migration is especially usefulin regions with complex geology, e.g., where the subsurface containssalt domes, faults, folds, etc.

Those of ordinary skill in the art will recognize that there are verylarge number of migration algorithms, each with its own strengths andweaknesses. However, certain kinds of noise can be troublesome dependingon the algorithm that is chosen. For example, migration “swing noise”,i.e., noise that cuts across multiple horizons, can be problematic incertain areas. Attenuating or eliminating this sort of noise and othersorts of noise such as multiples, refracted waves, etc., whilepreserving the underlying seismic signal, is a task that is not wellhandled by most migration algorithms.

As is well known in the seismic acquisition and processing arts, therehas been a need for a system and method that provides a better way tomigrate seismic data that does not suffer from the disadvantages of theprior art. Accordingly, it should now be recognized, as was recognizedby the present inventor, that there exists, and has existed for sometime, a very real need for a method of seismic data processing thatwould address and solve the above-described problems.

Before proceeding to a description of the present disclosure, however,it should be noted and remembered that the description which follows,together with the accompanying drawings, should not be construed aslimiting the disclosure to the examples (or embodiments) shown anddescribed. This is so because those skilled in the art to which thedisclosure pertains will be able to devise other forms within the ambitof the appended claims.

SUMMARY

According to one aspect, there is provided a system and method ofseismic exploration that makes it possible to image subsurfacestructures using controllable seismic sources that is superior to thatpreviously available.

According to one embodiment, subsurface ray directions in beam migrationare used to obtain additional Specular Filter (SF) and Dip OrientedPartial Imaging (DOPI) images. SF beam migration applies a specularimaging condition during migration with a pre-specified subsurface dipfield. This embodiment boosts the S/N ratio in both images and gathers,by effectively removing certain kinds of migration noise. In someembodiments, DOPI images are produced by decomposing a standard beammigration image according to subsurface dip inclination or/and dipazimuth groups. The decomposed partial images can be used to providevarious views of the subsurface image. Both SF and DOPI migration imagescan supply valuable additional information compared to a standardmigration image, and they can be efficiently generated in beammigration.

According to one embodiment, Specular Filter beam migration (SF-beam)uses only specular rays (with some specularity tolerance) to produce animage. It removes the non-specular rays from the imaging condition.SF-beam efficiently suppresses migration swing noise (that often cutsacross horizons), multiples, refraction waves, etc., produced by thenon-specular rays, while it preserves primary signals contributed by thespecular rays.

According to another embodiment, there is Dip-Oriented Partial Imagingbeam migration (DOPI-beam) that decomposes the full image into a fewsubsets of partial images according to the arriving ray directions(bisection of source and receiver sides) at subsurface image points.Decomposition criteria can be subsurface dip inclinations or dipazimuths or both. Such partial images provide different, complementaryviews of the subsurface by enhancing particular structural dips (definedby certain dip inclination and dip azimuth ranges), using well-separatedsubsurface illumination directions.

According to still another embodiment, the Specular Filter (SF) and DipOriented Partial Imaging (DOPI) concepts can be applied to other seismicmigration algorithms, including wavefield-based migration. Reverse timemigration (RTM) is an example of the wavefield-based migration, wherewavefields are generated and used for forming an image. Unlike ray-basedmigration, where ray propagation directions are computed duringmigration, the wave propagation directions are typically not available(or at least not automatically computed) in wavefield-based migration,such as RTM. To apply the SF and DOPI concepts to wavefield-basedmigration, accurate wave propagation directions need to be obtained fromwavefields. Optical flow is the method originally used in computervision to compute the apparent object movement directions from a video(or a series of pictures/still frames). In wavefield-based migrationsuch as RTM, the computed wavefields vary temporally and they can beanalyzed as though they were successive frames in a video work. Theoptical flow idea can be used in seismic migration to calculate the wavepropagation directions from a wavefield video. Therefore, according toan embodiment with an extra step of using optical flow to estimate wavepropagation directions in RTM, the workflow/procedures of SF or DOPI canbe applied to RTM almost the same way as SF or DOPI applied in beammigration.

According to an embodiment, there is provided a method of seismicexploration above a region of the subsurface of the earth containingstructural or stratigraphic features conducive to the presence,migration, or accumulation of hydrocarbons, wherein is provided aseismic survey collected proximate to the region of the subsurface ofthe earth, said seismic survey comprising a plurality of seismic traces,comprising the steps of: (a) accessing at least a portion of saidplurality of seismic traces; (b) obtaining a dip field associated withsaid accessed plurality of seismic traces, said dip field at least beingdefined at a plurality of (x,y,z) image points within said seismicsurvey; (c) specifying an upper specular index threshold and a lowerspecular index threshold different from said upper specular indexthreshold at each of said plurality of (x,y,z) image points; (d) foreach of said plurality of (x,y,z) image points, (i) obtaining one ormore source/receiver propagation direction pairs at said (x,y,z) imagepoint, (ii) for each of said obtained one or more source/receiverpropagation direction pairs at said (x,y,z) image point, using said dipfield to obtain an associated specular index value; (iii) determining anassociated upper specular index threshold and an associated lowerspecular index threshold associated with said (x,y,z) image point, (iv)selecting any source/receiver propagation direction pairs associatedwith said (x,y,z) image point that have a specular index value greaterthan said associated upper specular index threshold or less than saidassociated lower specular index threshold, and, (v) using any of saidselected source/receiver propagation direction pairs to form a migratedimage at said (x,y,z) image point, wherein said migrated images at saidplurality of (x,y,z) image points taken together form a migrated seismicdataset; and, (e) using at least a portion of said migrated seismicdataset in exploration for hydrocarbons within the region of thesubsurface of the earth.

According to another embodiment, there is provided a method of seismicexploration above a region of the subsurface of the earth containingstructural or stratigraphic features conducive to the presence,migration, or accumulation of hydrocarbons, wherein is provided aseismic survey collected proximate to the region of the subsurface ofthe earth, said seismic survey comprising a plurality of seismic traces,comprising the steps of: (a) accessing at least a portion of saidplurality of seismic traces; (b) accessing at least one dip inclinationrange; (c) accessing at least one dip azimuth range; (d) performing aDOPI migration using said accessed at least one dip inclination rangeand said accessed at least one dip azimuth range, thereby creating atleast one migrated seismic dataset; and, (e) using at least a portion ofsaid at least one migrated seismic dataset in exploration forhydrocarbons within the region of the subsurface of the earth.

According to still a further embodiment, there is provided a computersystem containing a plurality of computer instructions adapted toperform a method comprising: (1) accessing a plurality of seismic tracescollected proximate to a subsurface target; (2) performing a migrationon said plurality of seismic traces; (3) obtaining from said migrationan estimate of a dip field, said dip field at least being defined at aplurality of (x,y,z) image points proximate to said subsurface target;(4) specifying a specular index threshold greater than zero at each ofsaid (x,y,z) image points; (5) for each of said (x,y,z) image points,(i) obtaining one or more source/receiver propagation direction pairs atsaid (x,y,z) image point, (ii) using said dip field and any of said oneor more source/receiver propagation direction pairs at said (x,y,z)image point to obtain a specular index value associated with said any ofsaid one or more source/receiver direction pairs; (iii) determining aspecular index threshold associated with said (x,y,z) image point, (iv)selecting any source/receiver propagation direction pairs associatedwith said (x,y,z) image point that have an absolute value of saidobtained specular index value greater than said associated specularindex threshold, and, (v) using any of said selected source/receiverpropagation direction pairs to form a migrated image at said (x,y,z)image point, wherein said migrated images at each of said plurality of(x,y,z) image points taken together form an SF migrated seismic dataset;and, (6) using at least a portion of said SF migrated seismic dataset inexploration for hydrocarbons proximate to said subsurface target.

According to a further embodiment, there is provided a computer systemcontaining a plurality of computer instructions adapted to perform amethod comprising: (1) accessing a plurality of seismic traces collectedproximate to a subsurface target; (2) accessing at least one dipinclination range; (3) accessing at least one dip azimuth range; (4)performing a DOPI migration using said accessed at least one dipinclination range and said accessed at least one dip azimuth range,thereby creating at least one migrated seismic dataset; and, (5) usingat least a portion of said at least one migrated seismic dataset inexploration for hydrocarbons within the subsurface target.

According to still a further embodiment, there is provided a method ofseismic exploration above a region of the subsurface of the earthcontaining structural or stratigraphic features conducive to thepresence, migration, or accumulation of hydrocarbons, wherein isprovided a seismic survey collected proximate to the region of thesubsurface of the earth, said seismic survey comprising a plurality ofseismic traces, comprising the steps of: (a) accessing at least aportion of said plurality of seismic traces; (b) obtaining a dip fieldassociated with said accessed plurality of seismic traces; (c)specifying at least one specular index threshold less than 1.0; (d)using said dip field in conjunction with said at least one specularindex threshold to guide a specular filter migration applied to saidaccessed plurality of seismic traces, thereby creating a migratedseismic dataset; and, (e) using at least a portion of said migratedseismic dataset in exploration for hydrocarbons within the region of thesubsurface of the earth.

Other embodiments and variations are certainly possible within the scopeof the presently described embodiments and can readily be formulated bythose of ordinary skill in the art based on the disclosure herein.

The foregoing has outlined in broad terms the more important features ofembodiments disclosed herein so that the detailed description thatfollows may be more clearly understood, and so that the contribution ofthe instant inventor to the art may be better appreciated. Presentembodiments are not to be limited in its application to the details ofthe construction and to the arrangements of the components set forth inthe following description or illustrated in the drawings. Rather, otherembodiments may be practiced and carried out in various other ways notspecifically enumerated herein. Finally, it should be understood thatthe phraseology and terminology employed herein are for the purpose ofdescription and should not be regarded as limiting, unless thespecification specifically so limits an embodiment.

BRIEF DESCRIPTION OF THE DRAWINGS

Other objects and advantages will become apparent upon reading thefollowing detailed description and upon reference to the drawings inwhich:

FIG. 1 illustrates a general processing environment.

FIG. 2 contains an example processing sequence of the sort that might beutilized.

FIG. 3 contains a schematic illustration of how the specular index, si,is defined for use in SF migration according to one embodiment. Thedirection of the bisection vector (of the source and receiver rayarriving directions) in FIG. 3 is used in some embodiments fordecomposing a standard full image to produce DOPI migration images.

FIG. 4 contains a schematic illustration that makes clearer how oneembodiment operates on seismic data in connection with one variation ofthe SF migration embodiment.

FIGS. 5A-5C contain plots of specular index versus amplitudecontribution plot for image points 5A (zero amplitude), 5B (negativeamplitude), and 5C (positive amplitude) in FIG. 5D.

FIGS. 6A-6D provide a schematic illustration of some operational aspectssuitable for use with the SF migration aspect of the instantapplication.

FIG. 7 illustrates an operating logic suitable for use with anembodiment of SF migration.

FIG. 8 illustrates an operating logic suitable for use with anembodiment of the DOPI migration taught herein.

FIGS. 9A-9D contain an example of a DOPI beam migration over differentranges of dip angles.

FIGS. 10A-10E contain A DOPI beam migration example using differentranges of dip azimuths.

DETAILED DESCRIPTION

There is shown in the drawings, and will herein be described hereinafterin detail, some specific embodiments. It should be understood, however,that the present disclosure is to be considered an exemplification ofthe principles described herein and is not intended to be limited to thespecific embodiments or algorithms so described.

Embodiments

According to an embodiment, there is provided a system and method forimproving images derived from seismic data by improving the quality ofthe migration that is applied thereto.

Turning to FIG. 1, this figure contains a general overview of theinstant embodiment and its associated environment. As is indicated, aseismic survey will be designed 110 according to methods well known tothose of ordinary skill in the art. The survey might be, for example, aVSP (vertical seismic profile), land survey, marine survey, cross holesurvey, or some combination. Those of ordinary skill in the art willunderstand how surveys are designed and especially how such might bedone where the object is to image a particular subsurface target.

In the field, seismic data will be collected according to the surveydesign (block 120), possibly as-modified by conditions in the field.This will typically involve positioning source and receivers at leastapproximately according to the design and recording source activationsas is typically done. The recorded seismic waves (i.e., the seismicdata) may (or may not) be subjected to some in-field processing beforetransmitting it on to a processing center where the bulk of theprocessing will typically take place.

Typically within a processing center some initial processing will beperformed to associate each seismic recording with a surface or otherlocation (block 130), although some aspects of this block might alsohave been performed in the field. In either case, a computer system 150,which might be a workstation, a server, a main frame, a parallelcomputer, a networked collection of computers or workstations, etc.,will typically be engaged to process the data further in preparation forusing it in exploration.

Next, in some variations computer implemented algorithms that aredesigned to image the subsurface of the earth will be brought into oraccessed by the computer that is to apply them (item 140). Suchalgorithms, including algorithms suitable for implementing the instantembodiments, will typically be made available to a computer that is toutilize them via access to some amount of local or remote hard disk orother storage. Additional algorithms useful in the processing of seismicdata may be similarly provided to the CPU 150 which might be anyconventional or unconventional programmable computing device.

Conventionally, the seismic data will be processed and viewed on acomputer display such as that of a workstation 170. Output from theseismic processing may be used to create maps or plots of seismic dataand/or seismic attributes 180 according to methods well known to thoseof ordinary skill in the art.

FIG. 2 contains additional details of a typical seismic processingsequence suitable for use with seismic data collection 210, editing 215,some sort of initial processing 220, conditioning of the signal andimaging 230, production of imaged sections or volumes 240, initialinterpretation of the seismic data 250, further image enhancementconsistent with the exploration objectives 260, generation of attributesfrom the processed seismic data 270, reinterpretation of the seismicdata as needed 280, and ultimately generation of a drilling prospect290. In some embodiments, present embodiments might be implemented aspart of block 230 and/or 260. Of course, those of ordinary skill in theart will recognize that the foregoing are just examples of the manysorts of processing schemes that might be utilized with seismic data. Aparticular seismic processing sequence might use processes drawn fromsome or all of these blocks.

Turning next to a discussion of some aspects of an embodiment,subsurface ray directions in beam migration are used to derive noveladditional Specular Filter (SF) and Dip Oriented Partial Imaging (DOPI)images. SF beam migration applies a specular imaging condition duringmigration with a pre-specified dip field. It tends to boost the S/Nratio in both images and gathers, by effectively removing migrationnoise. In some embodiments, DOPI images are produced by decomposing astandard beam migration image according to subsurface dip inclinationor/and dip azimuth groups, providing various views of the subsurfaceimage. Both SF and DOPI migration images can supply valuable additionalinformation compared to a standard migration image, and they can beefficiently generated in beam migration.

In traditional migration, a traveltime table is the primary quantityused to map the surface recorded seismic signals to the subsurface imagespace for ray-based migration (e.g., Kirchhoff migration and beammigration). Green's functions are used instead for wave field-basedmigration (e.g., Wave Equation Migration (WEM) and Reverse TimeMigration (RTM)). However, ray/wave propagation directions can play avery important role in the imaging condition. By way of generalexplanation, Snell's law states that at any reflection point in anisotropic medium, the incidence angle and reflection angle are equal, orspecularity is honored. The rule holds true in an anisotropic medium tooif phase angles are utilized. As is discussed hereinafter in connectionwith present embodiments, the concept of specularity can be used as anadditional constraint for migration, resulting in an effectivesignal/noise or signal/signal separation.

The specular condition is not usually used for seismic migration in theindustry, but its importance has been widely recognized for ray-basedreflection tomography, where the velocity updates are back-projectedalong the ray paths determined by specularity at every RMO (i.e.,residual moveout) picking point. This helps insure that the invertedvelocity updates are placed at correct positions.

A previous study of coupling beam migration and reflection tomography(Zhang et al., 2012) introduced a new “QC image” concept for tomographyby forming an extra migration image using only specular rays (a verysmall fraction of all rays). Whether the QC image mimics the standardmigration image determines if the correct ray paths are chosen fortomography. In some cases this QC image can be “cleaner” and easier tointerpret. The disclosure of this reference is included as an appendixhereto and is incorporated herein by reference as if fully set out atthis point.

According to this aspect of present embodiments, the focus will be onthe QC image formed by specular rays, hereinafter referred to asSpecular Filter (SF) migration. It is worth noting that SF migration isa self-fulfilling process. It requires a-priori information of a dipfield, which can be measured from a previous standard migration image orinterpreted horizons (e.g., from picking or modeling). Spatially varyingdips with a specular tolerance value or spatially varying speculartolerance values for the whole-image-space SF migration are used, whichmakes it different from prior art approaches, where two constant dipbounds (namely, estimated horizon dip angle extremes) are used fortarget-zone migration. In some embodiments, SF beam migration isimplemented with both source and receiver rays shot downwards from theseismic recording/acquisition surface, which makes it different fromprior art approaches such as shooting rays upwards from image points.

According to another aspect of present embodiments, there is providedanother approach to migration, called Dip Oriented Partial Imaging(DOPI) migration, hereinafter. This approach decomposes a standardmigration image into subsets of partial images, based on potentialsubsurface dip inclinations or dip azimuths or both, that variousray/wave propagation directions try to illuminate. In other words, theDOPI image sub-volumes are categorized by the orientations of thevectors (see the bisection vector or illumination vector in FIG. 3) thatbisect actual source and receiver ray/wave propagation directions atimage points, based on the assumption that the potentially illuminatedreflector and the corresponding bisection vector are orthogonal (i.e.,specular condition).

In this way, DOPI migration separates horizons or faults according totheir varying orientations, and provides additional views of thesubsurface image from different angles. DOPI migration can also indicatepotential velocity errors when different image subsets do not focus thesame event. In contrast to SF migration, DOPI migration does not requirea pre-specified subsurface dip field.

SF and DOPI concepts can be applied in both ray-based migration andwavefield-based migration. In Kirchhoff migration and beam migration,obtaining ray directions costs almost nothing, because they are alwayspopulated with routine ray tracing. However, in WEM and RTM, wavepropagation directions are more difficult and costly to obtain. In thefirst part of this document, pre-stack depth beam migration is used as acarrier of SF and DOPI concepts.

The specular index si (Zhang et al., 2012, which reference isincorporated herein for all purposes as if fully set out at this point)is a key element for judging the specular condition in SF beammigration. It is defined by the value of cos(ø) in FIG. 3, that is, adot product of the dip normal vector and the normalized bisection vectorbetween the source and receiver ray arriving directions (in the case ofan isotropic medium) calculated through ray tracing, not from a constantray parameter assumption, at the image point. In anisotropic media thespecular index si is defined and applied the same way as in isotropicmedia, except the ray arrival directions are replaced by correspondingslowness vector directions. Said another way, the specular index isdetermined from a pair of propagation directions (one from a source andthe other from a receiver) in combination with the dip field at theimage point. In an embodiment, si will be in the range of [−1 1], andits value close to 1 (or −1) means the specular condition is reasonablywell honored. In practice, a threshold of |si|>0.9 or 0.8 will often bechosen for SF beam migration, which corresponds to a specular toleranceof about 26° or 37°, respectively. As a specific example, if thethreshold value is chosen to be 0.9 (i.e., |si|>0.9) and the dip fieldat a point is 30°, source/receiver ray pairs that potentially contributeto reflectors whose dip ranges are within the interval 30°±26° would bekept for imaging. Of course, the absolute value of the threshold mightbe chosen to be zero, in which case no thresholding would be performed.Because the estimated dip field typically varies spatially, the upperand lower dip thresholds can vary accordingly as a function of the(x,y,z) location. In some embodiments, a weighting scheme (such asGaussian, cosine or triangle tapers) instead of a hard threshold couldbe used. In other embodiments, there might be a plurality of thresholdsthat vary spatially at different (x,y,z) locations throughout the surveyregion. In still further embodiments, the thresholds might vary in timeand/or spatially.

A figure has been provided to make it clearer how SF migration works insome embodiments (FIG. 4). In this example, a dipping primary reflectorand its first order multiple are plotted, assuming a zero offsetacquisition and a constant velocity medium. Standard migration with onlya travel time constraint produces both the primary and multiplereflectors. In contrast, SF migration is capable of removing themultiple reflector with an extra constraint of specularity, because theprimary reflector is the only one that satisfies both constraints.

As shown in FIG. 4, multiple point A honors travel time but notspecularity, and B honors specularity but not travel time. Since in thisexample no points along the multiple reflector satisfy both, themultiple reflector will not be imaged by SF migration. On the otherhand, it is also clear that when the dip of primary reflector is gentle,SF migration may not do much with the multiple reflector. Note that,although only a demultiple case is explained, in practice, SF migrationmay be more effective in migration swing suppression. In someembodiments, it can also potentially remove refractions, head waves,converted waves, and etc.

Note further that thresholding si actually imposes a toleranceconstraint around the dip at the coordinate point (x,y,z) in thesubsurface, i.e., at points in space within the seismic survey coverage,where x and y refer to horizontal coordinates on the surface of theearth and z to a depth within the earth (or, alternatively, a one or twoway travel time since time and depth are generally interchangeable inseismic surveys). In other words, source/receiver ray pairs thatcontribute to reflectors whose dips (e.g., azimuth and/or inclinationdip) differ too much from the defined dip field at a point will beeliminated from the migration, depending on the selected value of thethreshold. Said another way, the dip field, which potentially varies ateach point in (x,y,z) space, guides the SF filter. This is one importantaspect of present embodiments.

Turning next to FIGS. 5A-5D, this figure provides an illustration of whythe specular condition can be an important consideration whenconstructing an image. In this particular embodiment, FIG. 5D is a crosssection (inline) of a single offset (˜4 km) 3D depth beam migrationimage from a synthetic marine NAZ dataset and its associated true TTIvelocity model. Three image points are chosen for this example: A) azero amplitude point not at any reflector; B) a negative amplitude pointat a reflector; and C) a positive amplitude point at a reflector (FIG.5D). The local dips were calculated at those three points, and fed intothe migration process. During migration, the amplitude contributionsfrom all possible arriving ray paths were collected at those threepoints and plotted against the horizontal axis in FIGS. 5A-5C,respectively. Also the si values from those contributing source/receiverray pairs were collected and aligned against the vertical axis. Specularrays (si close to 1) contribute large and dominant signals (circles inFIGS. 5B and 5C), while non-specular rays produce mostly noise (orsignals not supposed to be placed at those three points). In someembodiments, the noise from non-specular rays is expected to be canceledout during summation. However, an uneven ray path distribution may makethat difficult. Nonetheless, the non-specular rays can be ignored insome variations when constructing an image, which is the nature of SFmigration.

Turning next to FIG. 7, this figure contains an operating logic 700suitable for use with an instant embodiment of the SF migrationvariation. In one embodiment, the instant method will begin by accessingseismic data collected proximate to an exploration target of interest(block 705). In this embodiment, the seismic data will compriseunstacked seismic traces. It is desirable, but not necessarily required,that some amount of pre-processing will have been done to the seismictraces before migration (see, blocks 215 to 230 in FIG. 2).

Next, and as is conventionally done, in this embodiment a subsurfacevelocity model will be accessed or created according to methods wellknown to those of ordinary skill in the art (block 710). In somevariations, the explorationist or seismic processor will prepare thesubsurface velocity model for use with present embodiments. In othercases, a velocity model that has been developed for other purposes willbe accessed and used. It should be understood that this model may not beparticularly accurate, but it should at least be a rough approximationof the true/unknown subsurface velocities.

Next, according to an embodiment a migration will be performed using thevelocity model according to methods well known to those of ordinaryskill in the art (block 715). The migration algorithm that is used couldbe, by way of example only, a Kirchhoff migration, a beam migration, aone way wave equation migration, a reverse time migration, etc. In someembodiments two cubes of data will be obtained from the resulting image:a volume of inclination dip values and another volume of azimuthal anglevalues. In the SF migration embodiment, these cubes of data can be usedas the estimate of the dip field in the blocks that follow. In onevariation, the two cubes will each be the size of the image volume andwill be represented in the depth domain. Of course, those of ordinaryskill in the art will understand that, given the relevant velocities, itis straightforward to change from depth to time. Thus, when it is saidthat a trace is represented in depth (or time) it should be understoodthat it could as easily have been represented in the other domain, e.g.,time (or depth).

Next, in this embodiment the previously created subsurface dip fieldwill be accessed and/or estimated (block 720) if it has not been done soalready. In some cases the dip field estimate might be calculated frommigration as is explained above or, in other instances, supplied by auser. One approach to calculating the dip field would be to use asemblance or a plane-wave destruction method. Another approach would beto use interpreted horizons. In an embodiment, this dip information willbe calculated from images produced by standard migration or SFmigration, where the migration algorithm used might be Kirchhoffmigration, beam migration, one-way wave equation migration, or reversetime migration, etc.

Next, in some embodiments a specularity index threshold will be selectedor a weighting scheme specified (block 722). For example, in some casesthe threshold might be about |si|>0.9 or 0.8, which corresponds to aspecular tolerance of approximately plus or minus 26° or 37°,respectively. In this way the previously obtained dip field guides thespecular filter in that only source/receiver ray pairs that contributeto reflector dips within some specified angle/azimuth boundaries of thedip field will be used in the migration. In some embodiments, the imageamplitude contribution from a source/receiver ray pair might be weightedusing a Gaussian, cosine or triangle taper according to its specularindex or some other weighting scheme.

In this embodiment SF migration will next be performed (block 725). Inmore particular, in one embodiment the SF migration will be performedusing specular index thresholding or specular index weighting. In thisembodiment, SF migration and standard migration images can be generatedsimultaneously in this block if desired. SF migration according topresent embodiments could be implemented via Kirchhoff migration, beammigration, one-way wave equation migration, reverse time migration, etc.If the migration used is ray-based (e.g., beam migration) the raypropagation directions might be obtained directly from ray tracingduring the migration. If the migration was wavefield-based (e.g., RTM)as an example, the wave propagation directions might be obtained byoptical flow which is further discussed below. The resulting image willbe one that is formed predominantly from specular reflections.

During the SF migration a determination will be made at each image point(x,y,z) of the directions of the wave fronts that are approaching. Ininstances where the migration utilizes ray tracing, this will be the twolocal directions of the wave fronts at an image point as calculated fromrays traced from the source and receiver locations at the acquisitionsurface. In an anisotropic medium case, phase velocity directionsinstead of the ray directions could be used. See, e.g., FIG. 3 and thediscussion of si, supra.

Then, in an embodiment if the calculated magnitude of si for a pair ofwave fronts from a source and a receiver is less than a threshold value,that wave front pair won't be used in the image calculation at thatpoint. If, on the other hand, the value of si is greater than thethreshold value, that wave front pair will be used. Similarly, if thethreshold is expressed in terms of a taper, the wave front pair will beweighted by the taper value (which depends on si) before being added tothe image at that point.

The SF migrated image (block 725) will be useful in seismic exploration,monitoring, etc. (block 735) as would be any migrated dataset. The SFmigrated offset/angle gathers data might also be useful in tomographicor AVO/AVA analyses, etc. Additionally, though, because of the uniqueimaging qualities of the instant method, comparisons between datasetsprocessed by SF migration and conventional migration can give insightsinto and information about subsurface structures that may not be wellimaged on conventionally migrated datasets.

FIGS. 6A-6D continue the previous example and illustrate some aspects ofan embodiment of the foregoing using synthetic seismic data. Thesynthetic seismic dataset is intended to represent a cross line examplefrom a synthetic 3D dataset. First, run standard beam migration andproduce a stack image (FIG. 6A). Second, the dip field of the stackimage is estimated, in some embodiments, by a semblance or plane-wavedestruction method, or from interpreted horizons. The semblance methodis used in the example of FIG. 6B. Next, the dip field is supplied tothe SF beam migration algorithm which is used to generate the SF image(FIG. 6C). Note the subsalt reflectors (circles) are continuous in theSF migration image and the true horizon model (FIG. 6D), but not in thestandard migration image (FIG. 6A). The comparison between FIGS. 6A and6C also demonstrates that SF migration effectively removes migrationswings which may have been caused by the complex overburden, in thiscase, the salt body that was a part of the model (see the regionindicated by an arrow in FIGS. 6A-C).

Turning next to a discussion of an embodiment of the DOPI migrationalgorithm and as is generally set out in FIG. 8, in this variation ofthe method the same SF migration principle is used but it is applied ina different way. Instead of supplying the migration with a previouslyestimated structural dip field (which may or may not be particularlyaccurate) as in the case of SF migration, a limited range of subsurfacedip inclinations and azimuths is provided as an input to the DOPImigration. As a consequence of the specularity of the SF principleimplemented in DOPI migration, only those parts of the subsurface willbe well imaged that correspond to the processor-provided range of thesubsurface dip inclinations and azimuths. This can be repeated fordifferent ranges of dips/azimuths or their combinations, which can beachieved through either a single run of DOPI migration which yieldsmultiple DOPI outputs or multiple runs of a single-range/multi-rangeDOPI migration. In this way, the image of the subsurface can bedecomposed into different Dip-Oriented Partial Images (DOPI), each ofthem illuminating and enhancing subsurface features of different dipsand orientations. Note that if the specified dip angle and azimuthgroups cover the whole dip angle space [0°, 90° ] and the whole dipazimuth space [0°, 360° ], then the DOPI images form complete decomposedsubsets of a full image.

In FIG. 8, which illustrates one embodiment of the DOPI migration (block800), as before a seismic dataset will be accessed (block 805) and avelocity model constructed or obtained (block 810). Typically, thisdataset will have been pre-processed for migration. In some embodiments,this will be an unstacked seismic dataset collected proximate to anexploration target of interest.

Next, in an embodiment, ranges for subsurface dip inclination/azimuthgroups will be specified (block 815). That is, in one embodiment thefull inclination (i.e., 0° to 90°) will be divided into subsetinclination groups and/or the full azimuth (0° to 360°) will be dividedinto subset azimuth groups. It should be clear that more restrictedversion (e.g., inclination less than 90°, etc.) could be used instead.

Continuing with the example of FIG. 8, next the range restricted dipinclination/azimuth groups will be examined to determine which to use inperforming DOPI migration (block 820). In an embodiment, the group thatpotentially best images a particular reflector of interest (e.g., onethat is steeply dipping) will be identified. This is based on theassumption that the potentially imaged reflector dip orientation isorthogonal to the bisection direction of a source and receiver arrivingray pair at an image point. More particularly, the source/receiverpropagation direction pairs that image each point in the output image(x,y,z) will be calculated. The bisection direction associated with eachsource/receiver pair will be determined. This, then, can be used todetermine a dip azimuth (e.g., with respect to an arbitrary directionsuch as “north”) and a dip inclination (e.g., with respect to ahorizontal plane).

Using the SF principle in DOPI migration will tend to producewell-separated DOPI migration images (block 820). Multiple images ofDOPI migration and a standard migrated image can be generatedsimultaneously in this block with one migration run. The multiple images(one per inclination/azimuth range) might be subsequently combined(e.g., stacked) or not depending on how they are intended to be used. Insome embodiments, it might be desirable to view the uncombined imagesindividually to see certain dipping events in greater detail. DOPImigration is implemented in some embodiments via beam migration. Thatbeing said, those of ordinary skill in the art will understand that DOPImigration could be adapted to use Kirchhoff migration, one-way waveequation migration, reverse time migration, etc.

Finally, in an embodiment the DOPI migration images can be used byseismic interpreters (possibly after additional post processing, bock823) in exploration and other contexts (block 825) to provide additionalviews of the subsurface from specified illumination directions ascompared with standard migration images which cannot offer this ability.

The self-fulfilling characteristic of SF migration should be understoodto be a potential weakness of this approach: it heavily depends on thedip field provided. It is difficult to interpret all the horizons andfaults in a 3D image, so typically computers are relied upon for the dipfield estimation using, for example, semblance or plane-wavedestruction. But at the places of complex or conflicting dips, computerscan (and do) often get confused, yielding missed or misleading dips(e.g., dips measured from noise instead of signals). In FIG. 6C, a steepsalt flank is missing (arrow) due to the wrong dip field captured fromswing noise (arrow in FIG. 6B). In this situation, a manual correctioncould help. The computerized dip field also depends on the parameterschosen, such as the semblance window size, maximum dip allowed, andsmoothing window length. It is important for interpreters to understandthat the SF image could remove true primary events and filter outsignals instead of noise in some areas if used improperly. But the S/Nboost in most of a SF image can still be a very attractive tradeoff.

In addition to the two parameters mentioned above (the speculartolerance and the pre-specified dip field), a third parameter, beamwidth, influences the SF beam migration performance. The beam width, orvicinity index, defines how far away from the center ray path (from raytracing) beam migration extrapolates the traveltime table to, usingparaxial approximation in some embodiments. In SF beam migration, theray directions may be extrapolated as well, for example, using aspherical wave front assumption. Narrow beam width improves the accuracyof traveltime and ray directions, and therefore is preferred. But it cancreate traveltime table holes. Nonetheless, those of ordinary skill inthe art will recognize that the holes can be mitigated by finer surfacebins and shooting denser rays.

Turning to another embodiment, DOPI beam migration, in essence, alsorelies on the subsurface ray directions. In this embodiment itdecomposes the image by subsurface illumination directions (FIG. 3) atevery image point, yielding rigorous separations among image subsets.Although sharing a decomposition concept, DOPI migration differs fromthe Vector Offset Output (VOO) which only splits the image subsets bysimple vectors connecting shots to image points. Typically, DOPImigration decomposes the subsurface image according to subsurface dipinclinations or dip azimuths or their combinations. In some embodiments,each DOPI partial image provides a view of a single dipinclination/azimuth range at a time, preventing other dip interference.The summation of a complete set of decomposed DOPI partial images shouldamount at least approximately to the standard beam migration image.

FIGS. 9 and 10 contain examples of the application of DOPI migration toa synthetic dataset. FIGS. 9B, 9C and 9D are the decomposed imagesubsets according to dip angle ranges of [0° 20° ], [20° 40° ] and [40°90° ], respectively. These panels, when summed together, produce aresult that is comparable to a standard beam migration. FIG. 9A providesan example of a standard beam migration for purposes of comparison. EachDOPI panel provides a view of a restricted dip angle range, whichattenuates or eliminates interference from other dip angles. In theseDOPI image examples, ray directions have not been extrapolated and thusmay not be very accurate. As a consequence, there are some overlapsamong different angles ranges.

FIGS. 10A-10E contain an example of DOPI beam migration as applied tothe same dataset that was used in FIG. 9. In this case, an embodiment isapplied to different ranges of dip azimuths. FIGS. 10B, 10C, 10D and 10Eare the decomposed image subsets according to dip azimuth ranges of [0°90° ], [90° 180° ], [180° 270° ] and [270° 360° ] respectively. Theirsum is comparable to a standard beam migration which is provided in FIG.10A for purposes of comparison. Each DOPI image provides a view of adifferent dip azimuth range, which will tend to prevent interferencesfrom dip azimuths outside of the permitted range.

According to still another embodiment, there is provided a methodsubstantially as described herein but wherein an optical flow algorithmis used to calculate the wave propagation directions that can be used toextend the implementation of SF and DOPI to wavefield-based migrations,for example, reverse time migration. Optical flow algorithms are wellknown to those of ordinary skill in the art and, thus, will not bediscussed in any detail here. That being said, one source of algorithmsthat would be suitable for use with an embodiment would be theHorn-Schunck method (K. P. Horn and B. G. Schunck, “Determining opticalflow.” Artificial Intelligence, vol. 17, pp. 185-203, 1981), thedisclosure of which is incorporated herein by reference as if fully setout at this point.

By way of general background, in a video context the optical flowproblem seeks to solve every pixel's apparent velocity betweensuccessive image frames, and numerous approaches have been suggested.Here the Horn-Schunck methodology referenced above will be utilized inthis embodiment. However, since Horn-Schunck are focused on 2D, thediscussion that follows will be modified to be more suitable for usewith seismic data, i.e., a 3D variation will be taught below.

In reverse time migration (RTM), for example, if the 3D source (orreceiver) wavefields P₁(x,y,z) and P₂(x,y,z) at two successive times t₁and t₂, respectively are available, the task will be to find the motionvector M(x,y,z) at each grid point from P₁ to P₂.

At each grid point, the motion vector M(x,y,z) may be written as(u,v,w), where u, v, and w are the x, y, and z components of the motionvector, respectively, and

√{square root over (u ² +v ² +w ²)}

is the magnitude of the motion, or speed.

Now, assume, for purposes of this example, that

P(x,y,z,t)=P(x+Δx,y+Δy,z+Δz,t+Δt)

and by using the Taylor expansion, the optical flow equation can bewritten as follows:

P _(x) u+P _(y) v+P _(z) w+P _(t)=0,

where P_(x), P_(y), and P_(z) are the partial derivatives of thewavefield and P_(t) is the temporal derivative of the wavefield. Theyare defined as follows and estimates of these quantities can readily becalculated using P₁(x,y,z) and P₂(x,y,z).

${P_{x} = \frac{\partial P}{\partial x}},{P_{y} = \frac{\partial P}{\partial y}},{P_{z} = \frac{\partial P}{\partial z}},{{{and}\mspace{14mu} P_{t}} = {\frac{\partial P}{\partial t}.}}$

Continuing with the present example, the 3D Horn-Schunck method stepscan be described as follows when extended to 3D. The 3D version of thisalgorithm minimizes:

E=∫∫∫[(P _(x) u+P _(y) v+P _(z) w+P _(t))²+α²(∥∇u∥ ² +∥∇v∥ ² +∥∇w∥²)]dxdydz,

where ∥∇∥² is a spatial gradient operator defined as

${{\nabla }^{2} = {\left( \frac{\partial}{\partial x} \right)^{2} + \left( \frac{\partial}{\partial y} \right)^{2} + \left( \frac{\partial}{\partial z} \right)^{2}}},$

and α is a weighting factor. Continuing with the present example,minimization E above can be approached via the following iterativescheme:

${u^{n + 1} = {{\overset{\_}{u}}^{n} - \frac{P_{x}\left( {{P_{x}{\overset{\_}{u}}^{n}} + {P_{y}{\overset{\_}{v}}^{n}} + {P_{z}{\overset{\_}{w}}^{n}} + P_{t}} \right)}{\alpha^{2} + \left( P_{x} \right)^{2} + \left( P_{y} \right)^{2} + \left( P_{z} \right)^{2}}}},{v^{n + 1} = {{\overset{\_}{v}}^{n} - \frac{P_{y}\left( {{P_{x}{\overset{\_}{u}}^{n}} + {P_{y}{\overset{\_}{v}}^{n}} + {P_{z}{\overset{\_}{w}}^{n}} + P_{t}} \right)}{\alpha^{2} + \left( P_{x} \right)^{2} + \left( P_{y} \right)^{2} + \left( P_{z} \right)^{2}}}},{w^{n + 1} = {{\overset{\_}{w}}^{n} - \frac{P_{z}\left( {{P_{x}{\overset{\_}{u}}^{n}} + {P_{y}{\overset{\_}{v}}^{n}} + {P_{z}{\overset{\_}{w}}^{n}} + P_{t}} \right)}{\alpha^{2} + \left( P_{x} \right)^{2} + \left( P_{y} \right)^{2} + \left( P_{z} \right)^{2}}}},$

where ū, v and w are defined by:

${{\overset{\_}{u}}_{i,j,k} = {\frac{1}{32}\left( {{\sum_{{{ii} = {- 1}},0,1}{\sum_{{{jj} = {- 1}},0,1}{\sum_{{{kk} = {- 1}},0,1}u_{{i + {ii}},{j + {jj}},{k + {kk}}}}}} + u_{{i - 1},j,k} + u_{{i + 1},j,k} + u_{i,{j - 1},k} + u_{i,{j + 1},k} + u_{i,j,{k - 1}} + u_{i,j,{k + 1}} - u_{i,j,k}} \right)}},{{\overset{\_}{v}}_{i,j,k} = {\frac{1}{32}\left( {{\sum_{{{ii} = {- 1}},0,1}{\sum_{{{jj} = {- 1}},0,1}{\sum_{{{kk} = {- 1}},0,1}v_{{i + {ii}},{j + {jj}},{k + {kk}}}}}} + v_{{i - 1},j,k} + v_{{i + 1},j,k} + v_{i,{j - 1},k} + v_{i,{j + 1},k} + v_{i,j,{k - 1}} + v_{i,j,{k + 1}} - v_{i,j,k}} \right)}},{{\overset{\_}{w}}_{i,j,k} = {\frac{1}{32}\left( {{\sum_{{{ii} = {- 1}},0,1}{\sum_{{{jj} = {- 1}},0,1}{\sum_{{{kk} = {- 1}},0,1}w_{{i + {ii}},{j + {jj}},{k + {kk}}}}}} + w_{{i - 1},j,k} + w_{{i + 1},j,k} + w_{i,{j - 1},k} + w_{i,{j + 1},k} + w_{i,j,{k - 1}} + w_{i,j,{k + 1}} - w_{i,j,k}} \right)}},$

where, j and k are the spatial indices of x, y and z axes, respectively.

So, by way of summary of this embodiment, the 3D Horn-Schunk method canbe implemented as follows:

-   1. Obtain the input to the Horn-Schunck method: two successive    wavefields P₁(x,y,z) and P₂(x,y,z).-   2. Calculate the spatial and temporal derivatives from P₁(x,y,z) and    P₂(x,y,z): P_(x)=

$\frac{\partial P}{\partial x},{P_{y} = \frac{\partial P}{\partial y}},{P_{z} = {{\frac{\partial P}{\partial z}\mspace{14mu} {and}\mspace{14mu} P_{t}} = {\frac{\partial P}{\partial t}.}}}$

-   3. Choose a maximum number of iterations, N, and a weight α.    Generally speaking, N and α are data dependent, however, in the    absence of the data we typically choose N=20 and α=1).-   4. Set initial values u⁰=v⁰=w⁰=0.-   5. Use u^(n), v^(n) and w^(n) to calculate ū^(n), v ^(n) and w ^(n).-   6. Use ū^(n), v ^(n) and w ^(n) to calculate u^(n+1), v^(n+1) and    w^(n+1).-   7. Repeat steps (5) and (6) till the iteration counter reaches N    (i.e., n=0, 1, . . . , N−1).-   8. When the iteration count reaches N, (u^(n+1), v^(n+1), w^(n+1))    is the motion vector output between wavefields P₁(x,y,z) and    P₂(x,y,z).

Returning now to the embodiment of FIG. 7, according to anothervariation of this embodiment, one block involves the use of thecalculated dip field, specularity index and ray/wave propagationdirections to perform a filtered SF migration (block 725). Pursuant tothe present embodiment, the wave propagation directions will becalculated using an optical flow algorithm.

Similarly with respect to a variation of the embodiment of FIG. 8, oneblock involves the use of the dip inclination/azimuth ranges and theray/wave propagation directions to perform a DOPI migration (block 820).Pursuant to the present embodiment, the wave propagation directions willbe calculated using an optical flow algorithm as discussed above.

By way of explanation, SF/DOPI-beam and SF/DOPI-RTM migrations rely onthe accurate calculation of the ray/wavefield propagation directions.Calculating ray propagation directions in SF-beam and DOPI-beammigration can be done through ray-tracing as discussed previously.However, the calculation of wavefield propagation directions within a“reasonable” cost and a “reasonable” accuracy in the case of SF-RTM andDOPI-RTM can be challenging. There are a few Poynting-vector basedapproaches for obtaining wave propagation directions, however, stabilityof the directions obtained is a concern. The SF-RTM and DOPI-RTM methodborrows the optical flow concept from computer vision to produce stableand low-cost wave propagation directions. The Horn-Schunck method isused in one embodiment of the SF-RTM and DOPI-RTM code, and it runs muchmore efficiently compared to other (e.g., the Lucas-Kanade method) whichhas been used in other contexts to produce other products such as RTMangle gathers.

The SF image (e.g., SF beam migration image or SF RTM image) typicallyprovides a cleaner image to work with, however, the non-specular filter(NSF) image can also be valuable. The NSF image can be produced bysubtracting the SF image volume from the standard image volume. TheNSF-beam or NSF-rtm image provides a unique view of the imagedscattering points and, potentially, even the internal texture of rocks.The NSF-beam or NSF-rtm may be used to help understand, for example,rock fractures, faults, drilling hazards, and etc.

Certain methods taught herein provide a new seismic imaging conditionusing ray propagation directions at every subsurface image point thathas been implemented in beam migration to produce new image volumes. Inan embodiment, with a-priori information of the dip field measured froman existing migration image or interpreted horizons, Specular Filter(SF) beam migration effectively suppresses migration noise and makessignals stand out. Dip Oriented Partial Imaging (DOPI) beam migration,however, does not rely on the specification of dip information. Itdecomposes the standard image by subsurface dip inclinations or/andazimuths to provide extra views from certain illumination directions.Generating extra SF and DOPI images in standard beam migration does notneed much extra computation, but requires a fair amount of extra memoryfor ray direction storage. Besides beam migration, SF and DOPI conceptscan be adapted to Kirchhoff migration, WEM and RTM. In addition, the SFconcept may impact tomography and AVO work with by producing cleaner andhigher-quality common image gathers.

By way of summary, in one embodiment Filtered Beam Migration (SF-beam)uses only specular rays (optionally with some specularity tolerance) toproduce an image. If non-specular rays are removed from the imagingcondition, this will tend to suppress migration swing noise (that oftencuts across horizons), multiples, refraction waves, etc., produced bythe non-specular rays, while simultaneously preserving primary signalscontributed by the specular rays. This embodiment works well in bothsediment and salt related environments and typically results in a muchcleaner image.

One aspect of this embodiment is the specularity criterion that isevaluated for every image point and for every arriving ray pair. In oneembodiment it is measured by the dot product of the reflector normalvector and the normalized bisection direction of the arriving source andreceiver rays (in isotropic media) or corresponding slowness vectors (inanisotropic media). Although a dot product magnitude of 1.0 would meanperfectly specular, in practice, some tolerance will often be allowed,as an example, to take a dot product magnitude above 0.8 or 0.9 forimaging. Prior to migration, in some embodiments a subsurface dip fieldof the whole 3D image volume needs to be provided, which can beestimated from the previous migration stack.

This method could be compared with a local dip filtering algorithm bybeam migration. One prior art algorithm sets one upper and one lower dipboundary for a target area (subvolume) for application of the dipfiltering algorithm. The target area is migrated separately and latermerged with a normally migrated volume. Instead of using two constantdip boundaries as has been done in the prior art, the instant SF-beammigration method takes a spatially variable dip field estimated in thewhole volume (not a target area), which has different upper and lowerdip boundaries at different imaging points. In addition, the SF-beammethod applies to a whole image volume (i.e., not a target subvolume)without requiring a “cut off” and “paste back” of a target area.

Note that when the term “threshold” is used herein, that term should bebroadly construed to include instances where there the associated valuesmust satisfy a mathematical inequality (e.g., >0.9) as well as instanceswhere there is a transition from, say, unity to zero in an associatedweight function which might be, by way of example, a Gaussian, cosine,triangle, etc., taper. In some instances, the absolute value of thethreshold might be between zero and unity.

According to another aspect of the instant disclosure, there isDip-Oriented Partial Imaging beam migration (DOPI beam) that decomposesthe full image into a few subsets of partial images according toarriving ray directions (bisection of source and receiver sides) atsubsurface image points. Decomposition criteria can be subsurface dipinclinations or dip azimuths or both. Such partial images providedifferent, complementary views of the subsurface by enhancing particularstructural dip angles/azimuths, using well-separated subsurfaceillumination directions. DOPI-beam shares some similar concepts withVector Offset Output (VOO), but major difference exists. An embodimentof the DOPI beam approach uses accurate subsurface ray directions toilluminate a particular subsurface dip inclination/azimuth range at eachimage point, while VOO uses simple directions connecting sources toimage points. An estimated dip field based on a previous image is notrequired for DOPI beam migration.

For purposes of the claims that follow, the term “propagation direction”will generally be used to refer to a ray propagation direction (if raytracing is employed) or a wave propagation direction (if wave equationmigration is used) and should be interpreted accordingly depending onthe context in which it is used.

Additionally, those of ordinary skill in the art will understand thatseismic data can generally be accessed in terms of time, depth, orfrequency and, as such, techniques that are implemented in one domainand usually be implemented in the other two, albeit possibly withgreater (or lesser) computational complexity.

It should also be noted and remembered that the embodiments presentedherein are only given as examples and the teachings should not belimited to these examples unless specifically so indicated.

Further, although the text might have described the seismic sources as“guns”, “airguns”, etc., that was done only for purpose of illustrationand any marine source including, without limitation, air guns, waterguns, sparkers, boomers, chirp systems, water sirens, marine vibrators,etc., might potentially be used. Additionally, as noted previously theinstant disclosure could be applied on land as well. Further, instanceswhere the term “shot” was used herein should not be construed to limitthe instant disclosure to only operating with impulsive sources. Thus,when the term “seismic survey” is used herein that term should beunderstood to apply to a survey on water, land, or any combination ofsame.

Still further, where reference is made herein to a method comprising twoor more defined steps, the defined steps can be carried out in any orderor simultaneously (except where context excludes that possibility), andthe method can also include one or more other steps which are carriedout before any of the defined steps, between two of the defined steps,or after all of the defined steps (except where context excludes thatpossibility).

Further, when in this document a range is given as “(a first number) to(a second number)” or “(a first number)-(a second number)”, this means arange whose lower limit is the first number and whose upper limit is thesecond number. For example, 25 to 100 should be interpreted to mean arange whose lower limit is 25 and whose upper limit is 100.Additionally, it should be noted that where a range is given, everypossible subrange or interval within that range is also specificallyintended unless the context indicates to the contrary. For example, ifthe specification indicates a range of 25 to 100 such range is alsointended to include subranges such as 26-100, 27-100, etc., 25-99,25-98, etc., as well as any other possible combination of lower andupper values within the stated range, e.g., 33-47, 60-97, 41-45, 28-96,etc. Note that integer range values have been used in this paragraph forpurposes of illustration only and decimal and fractional values (e.g.,46.7-91.3) should also be understood to be intended as possible subrangeendpoints unless specifically excluded.

Further, it should be noted that terms of approximation (e.g., “about”,“substantially”, “approximately”, etc.) are to be interpreted accordingto their ordinary and customary meanings as used in the associated artunless indicated otherwise herein. Absent a specific definition withinthis disclosure, and absent ordinary and customary usage in theassociated art, such terms should be interpreted to be plus or minus 10%of the base value.

Additionally, while this disclosure is susceptible of embodiment in manydifferent forms, there is shown in the drawings, and will herein bedescribed hereinafter in detail, some specific embodiments. It should beunderstood, however, that the present disclosure is to be considered anexemplification of the principles of the system and method taught hereinand is not intended to limit it to the specific embodiments oralgorithms so described.

In the foregoing, much of the discussion has been discussed largely interms of conventional seismic surveys, but that was done for purposes ofillustration only and not out of an intent to limit the application orpresent embodiments to only those sorts of surveys. Those of ordinaryskill in the art will understand how the embodiments presented supracould readily be applied, by way of example, to 2D, 3D, 4D, etc.,surveys, down hole surveys, VSP surveys, cross hole surveys, or anycombination of same.

While embodiments of a device has been described and illustrated hereinby reference to certain embodiments in relation to the drawings attachedhereto, various changes and further modifications, apart from thoseshown or suggested herein, may be made therein by those skilled in theart, without departing from the spirit of the concept, the scope ofwhich is to be determined by the following claims.

What is claimed is:
 1. A method of seismic exploration above a region ofthe subsurface of the earth containing structural or stratigraphicfeatures conducive to the presence, migration, or accumulation ofhydrocarbons, wherein is provided a seismic survey collected proximateto the region of the subsurface of the earth, said seismic surveycomprising a plurality of seismic traces, comprising the steps of: (a)accessing at least a portion of said plurality of seismic traces; (b)accessing at least one dip inclination range; (c) accessing at least onedip azimuth range; (d) performing a DOPI migration using said accessedat least one dip inclination range and said accessed at least one dipazimuth range, thereby creating at least one migrated seismic dataset;and, (e) using at least a portion of said at least one migrated seismicdataset in exploration for hydrocarbons within the region of thesubsurface of the earth.
 2. The method of seismic exploration accordingto claim 1, wherein said at least one dip inclination range is selectedto be 0° to 90°.
 3. The method of seismic exploration according to claim1, wherein said at least one dip azimuth range is selected to be 0° to360°.
 4. The method of seismic exploration according to claim 1, whereinsaid at least one dip inclination range comprises three dip inclinationranges, and wherein said three dip inclination ranges are [0° 20° ],[20° 40° ] and [40° 90° ].
 5. The method of seismic explorationaccording to claim 1, wherein said at least one dip azimuth rangecomprises four dip azimuth ranges, and wherein said four dip azimuthranges are [0° 90° ], [90° 180° ], [180° 270° ] and [270° 360° ].
 6. Themethod of seismic exploration according to claim 1, wherein saidmigration is selected from the group consisting of a beam migration, aKirchhoff migration, a one-way wave equation migration, and a reversetime migration.
 7. The method according to claim 1, wherein step (d)comprises the steps of: (d1) accessing a velocity model defined at aplurality of (x,y,z) image points within said seismic survey; (d2) usingsaid velocity model to obtain at least one source/receiver propagationdirection pair at each of said plurality of (x,y,z) image points; (d3)for each of said at least one source/receiver propagation directionpairs determined at each of said plurality of (x,y,z) image points,obtaining a bisection direction; (d4) for each of said at least onesource/receiver propagation direction pairs determined at each of saidplurality of (x,y,z) image points, using said obtained bisectiondirection to determine an associated dip azimuth and an associated dipinclination; (d5) for each (x,y,z) image point, choosing thosesource/receiver propagation direction pairs whose associated dipinclination and dip azimuth are within one of said at least one dipinclination ranges and also within one of said at least one dip azimuthranges, and, (d6) for each (x,y,z) image point, using the chosensource/receiver propagation direction pairs to create an image at theassociated (x,y,z) image point, all of said images at said associated(x,y,z) image point together comprising a DOPI migration.
 8. A computersystem containing a plurality of computer instructions adapted toperform a method comprising: (1) accessing a plurality of seismic tracescollected proximate to a subsurface target; (2) accessing at least onedip inclination range; (3) accessing at least one dip azimuth range; (4)performing a DOPI migration using said accessed at least one dipinclination range and said accessed at least one dip azimuth range,thereby creating at least one migrated seismic dataset; and, (5) usingat least a portion of said at least one migrated seismic dataset inexploration for hydrocarbons within the subsurface target.
 9. The methodaccording to claim 8, wherein step (4) comprises the steps of: (i)accessing a velocity model defined at a plurality of (x,y,z) imagepoints proximate to said subsurface target; (ii) using said velocitymodel to obtain at least one source/receiver propagation direction pairat each of said plurality of (x,y,z) image points; (iii) for each ofsaid at least one source/receiver propagation direction pairs determinedat each of said plurality of (x,y,z) image points, obtaining a bisectiondirection; (iv) for each of said at least one source/receiverpropagation direction pairs determined at each of said plurality of(x,y,z) image points, using said obtained bisection direction todetermine an associated dip azimuth value and an associated dipinclination value; (v) for each (x,y,z) image point, choosing thosesource/receiver propagation direction pairs whose associated dipinclination value and dip azimuth value are within one of said at leastone dip inclination ranges and also within one of said at least one dipazimuth ranges, and, (vi) using the chosen source/receiver propagationdirection pairs to create an image at each of said plurality of (x,y,z)image points, all of said images created at each of said (x,y,z) imagepoints taken together comprising said DOPI migration.
 10. A tangible,non-transitory computer-readable medium configured to store instructionsexecutable by a processor of an electronic device to: access a beammigration image of a subsurface target; determine a decompositioncriteria based on at least one of subsurface dip inclinations,subsurface dip azimuths, or a combination thereof; decompose the beammigration image into a plurality of partial images according to thedecomposition criteria to provide various views of the subsurfacetarget, wherein the plurality of partial images are usable by seismicinterpreters in exploration for hydrocarbons within the subsurfacetarget.
 11. The non-transitory computer-readable medium of claim 10,wherein the plurality of partial images provide different, complimentaryviews of the subsurface target by enhancing particular structural dips.12. The non-transitory computer-readable medium of claim 11, wherein theparticular structural dips are defined by dip inclination ranges or dipazimuth ranges.
 13. The non-transitory computer-readable medium of claim10, wherein the beam migration image is generated based on a ray-basedmigration technique.
 14. The non-transitory computer-readable medium ofclaim 10, wherein the beam migration image is generated based on awavefield-based migration technique.
 15. The non-transitorycomputer-readable medium of claim 10, wherein the subsurface dipinclinations comprise a subset group of a whole dip angle space.
 16. Thenon-transitory computer-readable medium of claim 10, wherein thesubsurface dip azimuths comprise a subset group of a whole dip azimuthspace.
 17. The non-transitory computer-readable medium of claim 10,comprising instructions to combine the partial images for viewing. 18.The non-transitory computer-readable medium of claim 10, comprisinginstructions to transmit the partial images for individual viewing. 19.The non-transitory computer-readable medium of claim 10, wherein eachpartial image of the plurality of partial images comprises a single dipinclination or azimuth range at a particular time.
 20. Thenon-transitory computer-readable medium of claim 19, wherein eachpartial image of the plurality of partial images prevents dipinterference from at least one dip angle or dip azimuth.